HNS-RE2SD

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Higher National School of Renewable Energies, Environment and Sustainable Development

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    Damage Detection in PV Support Structures Using Smart Materials and Finite Element Analysis.
    (2025-06-15) SAHRAOUI Amina
    Structural Health Monitoring (SHM) is essential for ensuring the reliability and longevity of photovoltaic (PV) systems, particularly in detecting damage in their support structures. This study presents a numerical approach based on piezoelectric smart materials (PZT) and Finite Element Analysis (FEA) to detect structural damage. Various shapes and types of PZT elements were tested by bonding them to aluminum samples, and the square-shaped PIC151 was selected for its superior performance. A full 3D ground-mounted PV support structure was modeled in SolidWorks, and the inclined support column, which was identified as a structurally sensitive component, was selected for detailed simulation in ANSYS with an integrated PZT sensor. Several damage scenarios were simulated, including cracks of varying depth, orientation, dimensions, and position; corrosion represented by gradual material degradation; and overloading modeled using compressive forces. The variations in electromechanical impedance (EMI) responses were analyzed to evaluate the sensor’s capability in damage detection. Results showed that PZT sensors effectively distinguished between damage types: cracks produced sharp, localized changes in EMI signals; corrosion caused smoother frequency shifts, reflecting its cumulative nature; and overloading led to noticeable impedance variations due to internal deformations, which could be either temporary or permanent. Furthermore, a comparative physical analysis was conducted to differentiate the impedance responses associated with cracks, corrosion, and overloading, highlighting the distinct mechanical and material behaviors underlying each damage mechanism. These findings confirm the potential of PZT-based SHM techniques for accurate and cost-effective monitoring of PV support structures. The results of this study pave the way for future experimental validation on real-world solar panel support structures to confirm the simulation outcomes. This work can also be extended to various mounting types. Additionally, it is recommended to integrate the EMI technique with real-time smart monitoring systems to enhance damage detection capabilities and improve structural health management.
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    AI-Powered Platform for Automated Quiz and Question Generation.
    (2025-06-15) AISSAOUI Ayoub; BETTAHAR Akram; BOUREK Khalil; SAIGHI Ahmad Yasser
    This dissertation presents the design, implementation, and evaluation of an AI-powered platform developed for the automated generation of diverse assessment materials from user-provided PDF documents and raw text inputs. Addressing the significant time investment traditionally required for manual quiz creation in educational and training contexts, this work leverages advancements in Natural Language Processing (NLP) to streamline the process. The platform features a modular architecture implemented using Python and the Flask web framework, offering a user-friendly web interface for interaction. The core intelligence for generating Multiple-Choice Questions (MCQs) and Short Answer questions resides in a T5-base transformer model, specifically fine-tuned on the SQuAD v1.1 dataset adapted for question generation. This core model is supplemented by various NLP techniques and libraries (including NLTK, spaCy, and potentially Sense2Vec and Sentence-BERT based on implementation details) to facilitate the generation of Fill-in-the-Blanks questions, Matching tasks, and concise Summaries of the source material. The system allows users to specify the desired types and quantities of questions, providing flexibility in assessment creation. Evaluation of the fine-tuned T5 model demonstrated promising quantitative results, achieving average ROUGE-1 and ROUGE-L scores of 0.5247 and 0.4844, respectively, indicating a strong capability to capture semantic content and structure from the source text. While the average BLEU score was lower at 0.1988, this is often observed in generation tasks where content overlap (measured by ROUGE) is more critical than exact phrasal matching (measured by BLEU). Compared to existing commercial and academic solutions, the developed platform offers a distinct combination of input flexibility (PDF and text), a curated set of pedagogically relevant question types, and the use of an explicitly finetuned, adaptable T5 model. While acknowledging areas for future refinement, particularly concerning the robustness of PDF parsing across diverse formats and enhancing user interaction features, this project successfully demonstrates a viable and versatile approach to automating assessment generation. The platform holds significant potential to support educators, reduce administrative workload, and ultimately contribute to more dynamic and responsive learning environments.
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    Chimie 1 : Structure de la matière
    (HNS-RE2SD, 2022) CHABANI Sonia
    Chemistry is considered an integral part of the history of science and the contemporary world. In general, chemistry is the science that studies the composition, reactions, and properties of matter by examining the atoms that make up matter and their interactions with each other. This course material is primarily intended for first-year preparatory class students. It complies with the new reform program that came into effect in 2015. It is a powerful educational tool for students of science and technology or other specialties such as materials science, medicine, pharmacy, and biology to learn general chemistry. This course covers theoretical developments and uses mathematical tools to understand certain concepts of structural chemistry.
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    Fonctions électroniques 1
    (2025-06-01) Meddour Fayçal
    The objective of this course is to acquire basic theoretical knowledge of various electronic functions necessary for designing and implementing a transmission system. Topics covered include analog filters, amplitude, frequency, and phase modulation and demodulation, the impact of noise on the performance of these circuits, PLLs, etc.
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    New Tree Quantum Key Agreement Protocol and its Impact on IOT Networks
    (2024-06-15) EMZIANE Malak
    The Internet of Things is revolutionizing the way we interact with the world around us, creating a network of interconnected devices that communicate and share data easily. Quantum Key Agreement represents an exciting new area of cryptographic research, leveraging the principles of quantum mechanics to achieve secure communication. Unlike classical cryptographic methods, which rely on mathematical complexities, QKA utilizes the properties of quantum particles to ensure security. This makes QKA particularly resistant to the future improvements expected with the advent of quantum computing. In this dissertation, we explore the integration of a New Tree Multiparty Quantum Key Agreement within IoT networks. This protocol not only facilitates scalable key management but also enhances the overall security posture of IoT systems.
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    Sustainable Water Monitoring - Harnessing IoT and AI for Real-time Leak Detection and Water Quality Prediction
    (2024-06-15) DJEGHABA Mohammed Baha Eddine; BENTAHROUR Abir
    Water is an essential resource that supports both ecological sustainability and human survival, necessitating robust management strategies to meet increasing global demands, address climate change, and tackle environmental challenges. This thesis investigates the evolution of water supply monitoring systems, transitioning from traditional manual methods to advanced technologies using the Internet of Things (IoT) and Artificial Intelligence (AI). The central focus is on developing QoW-Pro, an IoT-based system that enhances water quality assessments and leak detection through AI algorithms. This system enables real-time data collection, predictive modeling, and anomaly detection, improving water resource management decisions. By integrating IoT with AI, the research offers a scalable and adaptable solution for various environments, aiming to ensure sustainable water management and quality for future generations.
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    Smart Electric Wheelchair for Real-time Obstacle Avoidance
    (2024-06-15) DIFALLAH Fayrouz
    The project involves developing a Smart Electric Wheelchair (SEW) to improve mobility and independence for individuals with physical disabilities. It uses a Raspberry Pi 4 Model B for processing, an L298 dual H-bridge motor driver, also ultrasonic sensors for obstacle detection, and servo motors for sensor positioning. Machine learning algorithms enhance real-time obstacle prediction and navigation safety. The system includes a robust communication subsystem and an Android application for user control.
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    AI-Based Security System Using YOLO Algorithms
    (2024-06-15) CHAHI Kamel Eddine
    Throughout the first chapter of this thesis, we have presented a comprehensive examination of security systems, addressing crucial considerations essential for designing robust solutions. We have highlighted key challenges faced by these systems, notably human errors leading to the oversight of significant events, the inherent complexity of system architecture, and the difficulties encountered during system extension and updates. This study offers an effective solution to the aforementioned challenges. By utilizing an AI card such as the Nvidia Jetson Nano, existing security camera systems can be transformed into intelligent and robust entities. This integration enhances their ability to process relevant events with high accuracy while potentially eliminating the need for additional equipment, replaced instead by the fusion of AI algorithms with the visual data captured by the cameras. The YOLOv8 model was trained using a large dataset downloaded from the Roboflow platform. Its images are labeled with seven classes: customer bagpack, null, product, product-picked, regular, shoplifting, and shopping cart. With a large configuration (43.7M parameters), we have obtained a good accuracy (0.9) and satisfactory convergence. However, during testing in different retail environments, challenges arose in accurately detecting certain products.
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    AIoT Based Smart Energy Monitoring System
    (2024-06-15) BOUTELIS Moussaab; ABDERRAHMANI Issam
    This project develops an AI-driven IoT-based Smart Energy Monitoring System to enhance energy efficiency in residential areas. Utilizing ESP32-CAM devices, it captures real-time images of utility meters and employs AI techniques like Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks to analyze and forecast energy consumption patterns. The system aims to help homeowners optimize resource use, minimize waste, and reduce expenses by providing immediate data insights and predictive analytics. It also improves utility companies’ data accuracy and customer trust through transparent reporting. The user-friendly web application is built on the Laravel framework, facilitating interactive data visualization. This initiative not only pushes technological boundaries but also promotes resource efficiency and scalability. Looking ahead, the project plans to integrate advanced machine learning algorithms, expand IoT capabilities for a fully connected home, and bolster security features, all contributing significantly to global sustainability objectives.
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    IIoT-Driven AI for Electrifcation Automation
    (2024-06-15) BOUTAA Ali
    In the midst of the AI era, the industrial domain is witnessing the increasing prominence of AI. This project aims to integrate AI into the Siemens system, taking a significant step towards Industry 4.0. By harnessing the power of AI, we will revolutionize electricity consumption prediction, enabling organizations to make informed decisions and optimize their energy usage. Our strategic approach includes analyzing unique business needs, preparing data, developing AI models, deploying them seamlessly, and continuously monitoring and improving their performance. We will leverage AI techniques such as artificial neural networks, genetic algorithms, and expert systems to transform the energy sector and support the growth and stability of Industry 4.0. This integration will empower organizations to make informed decisions, reduce their carbon footprint, and optimize energy usage, ultimately contributing to a more sustainable future. This project will enhance operational performance and productivity, increase competitiveness in the Industry 4.0 landscape, and pave the way for a more sustainable and technologically advanced industrial sector by streamlining decision-making processes and improving energy efficiency.